System and method for predicting vehicle component failure and providing a customized alert to the driver

ABSTRACT

Systems and methods for predicting component failure in a vehicle and alerting a driver based thereon. In an embodiment, the method includes receiving sensor data from at least one vehicle sensor over a period of time, processing the sensor data using a predictive model to detect an anomaly indicative of an upcoming component failure, determining a severity of the upcoming component failure based on at least one operating characteristic of the vehicle, and providing an alert to the driver of the vehicle regarding the severity of the upcoming component failure.

BACKGROUND Field of the Disclosure

The present disclosure generally relates to a system and a method forpredicting component failure in a vehicle and alerting a driver basedthereon. More specifically, the present disclosure relates to a systemand a method which predict the severity of the component failure andprovide a customized alert regarding the severity.

Background Information

Throughout the life of a vehicle, various components will deteriorateand/or fail. In some cases, these components can be repaired or replacedbefore causing serious damage to the vehicle. In other cases, use of thevehicle after a component has deteriorated or failed can result insignificant additional damage to other components of the vehicle. Thus,various component failures can result in significant consequences, suchas sudden vehicle failure, expensive repairs, driver anxiety, andincreased warranty costs.

SUMMARY

One object of the present disclosure is to provide systems and methodsthat can predict vehicle component failures with a high degree ofaccuracy, for example, using sensor data in accordance with variousmachine learning techniques. In doing so, the disclosed systems andmethods can prevent sudden vehicle failure, reduce repair costs, put thedriver's mind at ease, and/or reduce warranty costs by lessening therisk of severe failure due to the overuse of a failing component.

In view of the state of the known technology, one aspect of the presentdisclosure is to provide a method for predicting component failure in avehicle and alerting a driver based thereon. The method includesreceiving sensor data from at least one vehicle sensor over a period oftime, processing the sensor data using a predictive model to detect ananomaly indicative of an upcoming component failure, determining aseverity of the upcoming component failure based on at least oneoperating characteristic of the vehicle, and providing an alert to thedriver of the vehicle regarding the severity of the upcoming componentfailure.

Another aspect of the present disclosure is to provide an alternativemethod for predicting component failure in a vehicle and alerting adriver based thereon. The method includes receiving sensor data from atleast one vehicle sensor over a period of time, separating the sensordata into a plurality of characteristic data sets corresponding todifferent operating characteristics of the vehicle, processing at leastone of the characteristic data sets using a predictive model to detectan anomaly indicative of an upcoming component failure, and providing analert to the driver of the vehicle regarding the upcoming componentfailure.

Another aspect of the present invention is to provide a system forpredicting component failure in a vehicle and alerting a driver basedthereon. The system includes at least one vehicle sensor configured togenerate sensor data relating to at least one vehicle component over aperiod of time, a memory storing at least one operating characteristicof the vehicle and at least one predictive model that has been trainedusing time-series data from a plurality of other vehicles, and aprocessor configured to execute instructions stored on the memory to:(i) process the sensor data using the predictive model to detect ananomaly indicative of an upcoming component failure; (ii) determine aseverity of the upcoming component failure based on the at least oneoperating characteristic of the vehicle; and (iii) provide an alert tothe driver of the vehicle regarding the severity of the upcomingcomponent failure.

Other objects, features, aspects and advantages of the systems andmethods disclosed herein will become apparent to those skilled in theart from the following detailed description, which, taken in conjunctionwith the annexed drawings, discloses exemplary embodiments of thedisclosed systems and methods.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring now to the attached drawings which form a part of thisoriginal disclosure:

FIG. 1 is a schematic diagram of an example embodiment of a system forpredicting component failure in a vehicle and alerting a driver basedthereon in accordance with the present disclosure;

FIG. 2 illustrates an example embodiment of a method for predictingcomponent failure in a vehicle and alerting a driver based thereon inaccordance with the present disclosure;

FIG. 3 illustrates an example embodiment of the method of FIG. 2;

FIG. 4 illustrates another example embodiment of the method of FIG. 2;

FIG. 5 illustrates yet another example embodiment of the method of FIG.2;

FIG. 6 is a schematic diagram of an example embodiment of a processingarrangement for the system of FIG. 1;

FIG. 7 is a schematic diagram of another example embodiment of aprocessing arrangement for the system of FIG. 1;

FIG. 8 is a schematic diagram of yet another example embodiment of aprocessing arrangement for the system of FIG. 1; and

FIG. 9 is a schematic diagram of still another example embodiment of aprocessing arrangement for the system of FIG. 1.

DETAILED DESCRIPTION

Selected embodiments will now be explained with reference to thedrawings. It will be apparent to those skilled in the art from thisdisclosure that the following descriptions of the embodiments areprovided for illustration only and not for the purpose of limiting theinvention as defined by the appended claims and their equivalents.

FIG. 1 illustrates an example embodiment of a system 10 for predictingcomponent failure in a vehicle and alerting a driver. In the illustratedembodiment, the system 10 includes a vehicle 12 and a central controller14 which are wirelessly connected, for example, via a network. Asdescribed in more detail below, the central controller 14 is configuredto develop a predictive model PM using data from a plurality of vehiclesV which are similar to the vehicle 12 for which the predictive model PMis applied. The predictive model PM can then be used to predict thefailure of various components within the vehicle 12 and to thereafterprovide customized alerts to the driver.

As used herein, a “component” of a vehicle can refer to any part, groupof parts, system, subsystem and/or the like that can deteriorate and/orfail during use of the vehicle 12. Thus, any part, group of parts,system or subsystem of a vehicle 12, regardless of perceivedsignificance to the operation of the vehicle 12, can be considered a“component” as used herein. For example, a component can include one ormore of a light, a plug, a gasket, a sensor, a converter, a belt, ahose, a brake, a brake pad, a water pump, a fuel pump, a tire, abattery, one or more elements of a suspension, electronics, atransmission system, a monitoring system, an engine, and/or individualcomponents thereof or larger systems including these smaller components.

As illustrated, the vehicle 12 can include a vehicle body 20 and avehicle controller 22. The vehicle body 20 can include a user interface24 and one or more sensor 26 (e.g., sensors 26 a, 26 b . . . 26 n),which can each be placed in wired or wireless communication with thevehicle controller 22. The vehicle body 20 can further include anavigation system 28 placed in wired or wireless communication with thevehicle controller 22 and/or the user interface 24. In accordance withthe methods discussed herein, the vehicle controller 22 is configured togather sensor data 30 from one or more sensor 26, process the sensordata 26 using a predictive model PM or transmit the sensor data 26 tothe central controller 14 for further processing using the predictivemodel PM, and cause an alert to be generated on the user interface 24depending on the result of the processing. In an embodiment, the alertcan include information based on location data generated using thenavigation system 28.

The vehicle controller 22 can include one or more of a vehicle processor32, a vehicle memory 34, and a data transmission device 36. Variousexample embodiments of the vehicle controller 22 are illustrated byFIGS. 6 to 9, although these embodiments are not intended to belimiting. The vehicle processor 32 is configured to execute instructionsprogrammed into and/or stored by the vehicle memory 34. As described inmore detail below, many of the steps of the methods described herein canbe stored as instructions in the vehicle memory 34 and executed by thevehicle processor 32. The vehicle memory 34 can include, for example, anon-transitory storage medium. The data transmission device 36 caninclude, for example, a transmitter and a receiver configured to sendand receive wireless signals to and from the central controller 14 inaccordance with methods known in the art. For example, the datatransmission device 36 can be configured for short-range wirelesscommunication, such as Bluetooth communication, and/or for communicationover a wireless network.

The user interface 24 can include one or more of a display 38 and aninput device 40. In an embodiment, the display 38 and the input device40 can be part of a graphical user interface such as a touch screenwhich enables a driver to input and view information regarding variousaspects of the vehicle 12. The display 38 is configured to display, forexample, the alerts generated by the methods discussed herein. In anembodiment, the user interface 24 can further enable the driver of thevehicle 12 to access the navigation system 28, for example, to navigatethe vehicle 12 to a repair shop based on the alerts generated by themethods discussed herein.

The one or more sensor 26 can include a plurality of sensors 26configured to generate various sensor data 30 regarding operation of thevehicle 12. In an embodiment, each sensor 26 is configured to generatesensor data 30 relating to at least one vehicle component over a periodof time. In FIG. 1, the sensors 26 are illustrated as a first sensor 26a, a second sensor 26 b, and an nth sensor 26 n. Those of ordinary skillin the art should understand from this disclosure that any number ofsensors 26 can be used, and that a vehicle 12 can include hundreds ofsuch sensors 26. The types of sensors 26 can vary depending on the typeof vehicle 12 and its features. The examples discussed herein aregenerally described with respect to a vehicle 12 include eighty (80)sensors 26, but these examples are not intended to be limiting. Aplurality of sensors 26 can include, for example, an injector sensor, avaporizing sensor, an A/F sensor, a fuel pump sensor, a valve sensor, abattery sensor, an ignition coil sensor, a contact sensor, a plugsensor, a valve sensor, a timing control sensor, a motion sensor, and/orany other sensor configured to generate sensor data 30 in accordancewith the methods discussed herein.

In the illustrated embodiment, the vehicle 12 further includes a CAN(“Controller Area Network”) bus 31 which connects the plurality ofsensors 26 to the vehicle controller 22. Thus, the sensor data 30 fromthe sensors 26 can include CAN signals from the CAN bus 31. Those ofordinary skill in the art will also recognize from this disclosure thatother configurations are possible.

The vehicle navigation system 28 can include a GPS device 42 or beplaced in communication with a GPS device 42. The GPS device 42 can beconfigured to determine location data regarding the physical location ofthe vehicle 12 and communicate the location data to the vehiclecontroller 22 and/or the central controller 14 for use in accordancewith the methods discussed herein. The GPS device 42 can determine thelocation data, for example, via communication with one or more globalpositioning satellite as known in the art. The GPS device 42 can alsodetermine the location data, for example, via communication with one ormore terrestrial units and a base station or external server. In anembodiment, the navigation system 28 can further be placed in operativecommunication with the user interface 24, such that the user interface24 allows the driver of the vehicle 12 to input (e.g., select) adestination address using the input device 40 and utilize the navigationsystem 28 to generate route information that is displayed on the display38.

During use of the vehicle 12, the vehicle controller 22 is configured tocontinuously or periodically collect and store sensor data 30 using thevehicle memory 34. The sensor data 30 can include data from the CAN bus31 that is connected to the plurality of sensors 26. As discussed inmore detail below, the vehicle controller 22 is configured to collectand store a plurality of data points from the sensor data 30 over aseries of time. For example, the vehicle controller 22 can collect firstsensor data 30 a from the first sensor 26 a over a series of time (e.g.,every 1 second, 0.1 second, etc.), second sensor data 30 b from thesecond sensor 26 b over the series of time, and nth sensor data 30 nfrom the nth sensor 26 n over the series of time. The sensor data 30from the plurality of sensors 26 can be stored, for example, using oneor more data matrix 44 which includes data points over one or more timeintervals. FIG. 1 illustrates an example of one such data matrix 44, inwhich a plurality of data points for the first sensor data 30 a, thesecond sensor data 30 b, and the nth sensor data 30 n are shown over atime interval.

The central controller 14 can include one or more of a central processor52, a central memory 54, and a data transmission device 56. The centralprocessor 52 is configured to execute instructions programmed intoand/or stored by the central memory 54, and many of the steps of themethods described herein can be stored as instructions in the centralmemory 54 and executed by the central processor 52. The central memory54 can include, for example, a non-transitory storage medium. The datatransmission device 56 can include, for example, a transmitter and areceiver configured to send and receive wireless signals to and from thevehicle controller 22 in accordance with methods known in the art. Forexample, the data transmission device 56 can be configured forshort-range wireless communication, such as Bluetooth communication,and/or for communication over a wireless network.

FIG. 2 illustrates an example embodiment of a method 100 for predictingcomponent failure in a vehicle 12 and alerting a driver. Some or all ofthe steps of method 100 can be stored as instructions on the vehiclememory 34 and/or the central memory 54 and can be executed by thevehicle processor 32 and/or the central processor 52 in accordance withthe respective instructions stored on the vehicle memory 34 and/or thecentral memory 54. It should be understood from this disclosure thatsome of the steps described herein can be reordered or omitted withoutdeparting from the spirit or scope of method 100.

In FIG. 2, method 100 is divided into a first portion 110 and a secondportion 120. The first portion 110 of the method 100 relates to thegeneration of a predictive model PM prior to implementation by aspecific vehicle 12, and the second portion 120 of the method 100relates to implementation using the specific vehicle 12. It should beunderstood from this disclosure that each of the first portion 110 andthe second portion 120 can constitute independent methods, and that thefirst portion 110 and the second portion 120 are shown together in FIG.2 for simplicity.

Referring first to the first portion 110 of the method 100, the firstportion 110 relates to the generation of a generic predictive model PMusing information from a plurality of vehicles V. Once generated, thepredictive model PM can be applied by a plurality of vehicles 12. Itshould be understood from this disclosure that the vehicles 12 can be asubset of the vehicles V from which data is collected to create thegeneric predictive model PM (e.g., vehicle 12 can be a specific makeand/or model of the vehicles V, a specific year of the vehicles V, aspecific version of the vehicles V, etc.). The steps of the firstportion 110 of the method 100 can be performed entirely by the centralcontroller 14, or can be performed by the central controller 14 incombination with one or more vehicle controller 22.

At step 112, the central controller 14 collects sensor data 30 from aplurality of vehicles V. Each vehicle V can also be a vehicle 12. Thatis, the vehicles V can be the same or similar to the vehicle 12 forwhich the method 100 will eventually be applied (e.g., same make/model,same year, same version, etc.). Alternatively, the vehicle 12 can beamong a subset of the vehicles V. Each vehicle V can include any or allof the components illustrated with respect to the vehicle 12 in FIG. 1.

The sensor data 30 from the vehicles V can include time-series dataoutput from one or more sensor 26 over a period of time. Each period oftime can be, for example, related to a trip made by the vehicle 12. Asused herein, a “trip” can correspond, for example, to use of a vehicle Vor vehicle 12 beginning at a first location and ending at a secondlocation. In an embodiment, a plurality of trips can occur between aninitial location and a destination location based on operatingcharacteristics that vary between the initial location and thedestination location. Thus, a “trip” can be defined by the operatingcharacteristics of a vehicle V or vehicle 12 over a period of time. Foreach trip that a vehicle V or vehicle 12 takes, sensor data 30 can becollected and associated with one or more operating characteristic forthe vehicle V or vehicle 12 during that trip. The operatingcharacteristics can be determined by the central controller 14 or avehicle controller 22 associated with each vehicle V and can include,for example, one or more of: (i) speed; (ii) acceleration; (iii)revolutions per minute; (iv) torque; (v) oil temperature; and (vi)number of braking or acceleration events.

At step 114, the central controller 14 can sort the sensor data 30 intoa plurality of data sets 60. If the vehicle 12 is among a subset of thevehicles V for which sensor data 30 was collected, the sensor data 30from vehicles V that are the same as or similar to the vehicle 12 canfirst be separated from that of other vehicles V. Then, in variousembodiments, the sensor data 30 from similar vehicles V can be sorted byone or more of: (i) time period of collection; (ii) normal or abnormaloperation of the vehicle during collection; and/or (iii) one or moreoperating characteristic sensed during collection.

In an embodiment, the data sets 60 can include normal data sets 60N fornormal data and abnormal data sets 60A for abnormal data. The normaldata can include, for example, sensor data 30 recorded by a vehicle Vduring a period of time that the vehicle V operated without anycomponent failures. For example, the normal data can encompass periodsof time that fall between manufacture of the vehicle V and apredetermined period of time before any component failure occurred. Theabnormal data can include, for example, sensor data 30 recorded by avehicle V during a period of time leading up to a confirmed componentfailure. The beginning of the abnormal period of time can be, forexample, a time when sensor data 30 from one or more sensor 26 began todeviate leading up to a confirmed component failure. Alternatively, thebeginning of the abnormal period of time can be a predetermined amountof time prior to a confirmed component failure.

In an embodiment, the data sets 60 can include characteristic data sets60C which separate the sensor data 30 by one or more operatingcharacteristic. The characteristic data sets 60C can be a subgroup ofthe normal data sets 60N and/or abnormal data sets 60A, such that thecentral controller 14 sorts the sensor data 30 into normal data sets 60Nand abnormal data sets 60A which are each associated one or moreoperating characteristic. The operating characteristic can be anyvariable characteristic experienced by the vehicle V during a trip. Inan embodiment, the characteristic data sets 60C can separate the sensordata 30 based on correspondence with a value or range of at least oneof: (i) trip speed; (ii) acceleration; (iii) revolutions per minute;(iv) torque; (v) oil temperature; and/or (vi) number of braking oracceleration events. For example, an operating characteristic caninclude one or more of: (i) an average speed of the vehicle Vexperienced during a trip; (ii) an average acceleration of the vehicle Vexperienced during a trip; (iii) an average revolutions per minuteexperienced by the vehicle V during a trip; (iv) an average torqueexperienced by the vehicle V during a trip; (v) an average oiltemperature experienced by the vehicle V during a trip; and/or (vi) anaverage number of braking or acceleration events experienced by thevehicle V during a trip. Thus, for example, normal data sets 60N can besorted into separate characteristic data sets 60C relating to ranges ofaverage speed (e.g., a first data set 60 a for trips with an averagespeed between 1 to 20 mph, a second data set 60 b for trips with anaverage speed of 20 to 40 mph, etc.). In another example, normal datasets 60N can be sorted into separate characteristic data sets 60Crelating specific average speeds (e.g., a first data set 60 a of sensordata 30 recorded during an average speed of 10 mph, a second data set 60b of sensor data 30 recorded during an average speed of 20 mph, etc.).

Each data set 60, regardless of the particular thresholds for sorting,can include sensor data 30 from a plurality of sensors 26 taken over aperiod of time. FIG. 2 at step 114 illustrates an example embodiment ofa data set 60 which includes sensor data 30 from a plurality of sensors26 over a specified time interval (e.g., for a trip). Here, the data set60 includes sensor data 30 from each of eighty (80) sensors 26 over thesame time interval. In other words, the data set 60 includes time-seriessets of sensor data 30 for each of a plurality of sensors 26. The dataset 60 shown in FIG. 2 could be, for example, the first data set 60 a orthe second data set 60 b discussed in the previous paragraph. The dataset 60 shown in FIG. 2 could also be, for example, a normal data set60N, an abnormal data set 60A, or a characteristic data set 60C.

At step 116, the central controller 14 develops one or more predictivemodel PM using the data sets 60. In developing the predictive model PM,the central controller 14 trains the predictive model PM to detect oneor more anomaly in a vehicle 12 using the time-series data from aplurality of similar vehicles V. In an embodiment, a separate predictivemodel PM can be developed for each of a plurality of data sets 60. Forexample, a first predictive model PM1 can be developed for a firstoperating characteristic, a second predictive model PM2 can be developedfor a second operating characteristic, etc. As described in more detailbelow, developing the one or more predictive model PM can also includedetermining one or more threshold which indicates an upcoming componentfailure and/or a severity of the component failure.

In an embodiment, the one or more predictive model PM can include one ormore neural network, and developing the one or more predictive model PMcan include training the one or more neural network using the data sets60. Training the neural network can include using data sets 60 relatedto normal data to train the neural network how to view sensor data 30when a vehicle component is working properly. Training the neuralnetwork can further include using data sets 60 related to abnormal datato train the neural network how to view sensor data 30 when a specificvehicle component is approaching failure. Training the neural networkcan further include using data sets 60 related to different vehicleoperating characteristics to train the neural network to determine aseverity of the upcoming component failure based on at least oneoperating characteristic of the vehicle. In other words, training theneural network can include determining how different operatingcharacteristics contribute to (e.g. slow down or hasten) a componentfailure.

One or more predictive model PM can be trained, for example, using thenormal data sets 60 as inputs for normal behavior by a vehicle 12. In anembodiment, numerical values taken directly from the data sets 60 can beused as an input for the predictive model PM. In another embodiment,values or equations calculated from the data sets 60 can be used as aninput for the predictive model PM. In another embodiment, images orgraphs created from the data sets 60 can be used as an input for thepredictive model PM. Various embodiments which determine correlations ordistribution statistics or create images or graphs from the data sets 60are discussed in more detail below.

At step 118, the predictive model PM is stored for later use byindividual vehicles 12. In one embodiment, the central controller 14 canstore one or more predictive model PM using the central memory 54 (e.g.,as seen in FIG. 6). In another embodiment, the central server 14 cantransmit one or more predictive model PM to the vehicle controller 22 ofeach vehicle 12, such that one or more predictive model PM is stored bythe vehicle 12 using the vehicle memory 34 (e.g., as seen in FIGS. 7 to9). Alternatively, one or more predictive model PM can be stored byanother memory separate from the vehicle memory 34 or the central memory54, wherein the separate memory is thereafter placed in communicationwith the central controller 14 or the vehicle controller 22 for furtheruse of the predictive model PM as discussed herein.

Referring next to the second portion 120 of the method 100, the secondportion 120 relates to implementation of the predictive model PM using aspecific vehicle 12. The steps of the second portion 120 of method 100can be performed by the vehicle controller 22 after storing thepredictive model PM using the vehicle memory 34. Alternatively, thesteps of the second portion 120 of method 100 can be performed by acombination of the central controller 14 and the vehicle controller 22.Various example embodiments of the structure for implementing the secondportion 120 of the method 100 are illustrated by FIGS. 6 to 9.

At step 121, the central controller 14 and/or the vehicle controller 22receives initial sensor data 30 from a vehicle 12. The initial sensordata 30 can include the same type of sensor data 30 used to develop thepredictive model PM at step 116 (e.g., can be sorted into data sets 60which correspond to the data sets 60 used at step 116). Step 121 can beperformed, for example, when the vehicle 12 is first manufactured andall of its components are operating in normal working condition. Forexample, step 121 can be performed before the vehicle 12 is shippedafter manufacture and/or over a predetermined distance first driven bythe vehicle 12 (e.g., the first of a predetermined number ofmiles/kilometers driven by the vehicle 12). Here, the initial sensordata 30 is intended to provide a normal operation baseline to calibratethe predictive model PM for later use by the specific vehicle 12.

At step 122, the predictive model PM can be calibrated for the vehicle12. The calibration is performed so that the predictive model PM isaccurate for the vehicle 12. In an embodiment, the predictive model PMcan be for a group of similar vehicles V, and the calibration can applythe predictive model PM to a specific vehicle 12 within that group. Evenamongst the same type of vehicles 12, each distinct vehicle 12 caninclude sensors 26 with slightly different sensitivities or features,making calibration for a specific vehicle 12 still desirable in certaincases even when the predictive model PM is already trained for the sametype as the vehicle 12. Thus, even if the predictive model PM is alreadypre-calibrated for a make and model that is the same as the vehicle 12,it can be helpful to calibrate each predictive model PM for eachspecific vehicle 12 for later use. In an embodiment, the centralcontroller 14 or the vehicle controller 22 can calibrate the predictivemodel PM by applying weights to inputs into the predictive model PMwhich are based on the sensor data 30 from the vehicle 12.Alternatively, the central controller 14 or the vehicle controller 22can apply the weights to values stored within the predictive model PM soas to fit the predictive model PM to the sensor data 30 generated by thevehicle 12.

The calibrated predictive model PM can be stored for later use by thevehicle 12. In one embodiment, the central controller 14 can store thecalibrated predictive model PM using the central memory 54 (e.g., asseen in FIG. 6). In another embodiment, the vehicle controller 14 canstore the calibrated predictive model PM using the vehicle memory 34(e.g., as seen in FIGS. 7 to 9). In another embodiment, the calibratedpredictive model PM can be stored by another memory separate from thevehicle memory 34 or the central memory 54, wherein the separate memoryis thereafter placed in communication with the central controller 14 orthe vehicle controller 22 for further use of the calibrated predictivemodel PM as discussed herein.

At step 123, the central controller 14 and/or the vehicle controller 22receives sensor data 30 from at least one vehicle sensor 26 of thevehicle 12 over a period of time. For example, the sensor data 30 can begenerated during regular use of the vehicle 12 for day-to-dayactivities. In an embodiment, step 123 can be continuously performed bythe vehicle controller 22 at all times when the vehicle 12 is in use. Inthis way, the second portion 120 of method 100 is continuously performedover the life of the vehicle 12 so that component failures are predictedat most or all times during use of the vehicle 12 by the owner.

In an embodiment, the vehicle memory 34 can store the operatingcharacteristics recorded during collection of the sensor data 30 ashistorical operating characteristics. These historical operatingcharacteristics can optionally be accessed later to estimate the urgencyof a component failure based on how the vehicle 12 is typically drivenby the owner.

At step 124, the central controller 14 and/or the vehicle controller 22processes the sensor data 30 using one or more predictive model PM todetect an anomaly indicative of an upcoming component failure for thevehicle 12. The one or more predictive model PM can include thecalibrated predictive model PM from step 122. In processing the sensordata 30, at least one of the central controller 14 or the vehiclecontroller 22 can sort the sensor data into various data sets 60 andprocess the data sets 60. The data sets 60 can be of the same type usedto develop the predictive model PM at step 116 and/or of the same typeused to calibrate the predictive model at step 122. For example, thedata sets 60 can include characteristic data sets 60C divided accordingto one or more operating characteristic such as: (i) average trip speedof the vehicle V experienced during a trip; (ii) average acceleration ofthe vehicle experienced during a trip; (iii) average revolutions perminute experienced by the vehicle during a trip; (iv) average torqueexperienced by the vehicle during a trip; (v) average oil temperatureexperienced by the vehicle during a trip; or (vi) average number ofbraking or acceleration events experienced by the vehicle during a trip.The predicted component failure can relate to any vehicle componentbeing rendered in a state that it cannot effectively function as desired(e.g., a dead battery, a failed plug or valve, an issue with thetransmission, a nonfunctioning sensor, a faulty ignition coil, etc.).

An anomaly can be determined, for example, when one or more thresholddetermined using the predictive model PM is crossed, e.g., by the sensordata 30 generated during step 124 or other values based thereon. Severalexamples of thresholds are discussed in more detail below. In variousembodiments, the thresholds for determining an anomaly can be determinedat step 116 when the predictive model PM is developed and/or at step 122when the predictive model PM is calibrated for the vehicle 12. In anembodiment, each operating characteristic or groups of operatingcharacteristics can have different thresholds for anomaly detection.Thus, a predictive model PM for a first set of operating characteristicscan have a different threshold than a predictive model PM for a secondset of operating characteristics. In another embodiment, multiplethresholds can be determined during step 116 and/or step 122, with eachof the multiple thresholds relating to a severity of component failureand/or one or more operating characteristic associated with the vehicle12.

At step 125, the central controller 14 and/or the vehicle controller 22determines the severity of the upcoming component failure based on theoutput from the predictive model PM. The severity of the upcomingcomponent failure can include one or more of: (i) the likelihood of theupcoming component failure; (ii) the expected time or distance remaininguntil the upcoming component failure (e.g., expected hours/daysremaining or an expected number of miles/kilometers driven untilfailure); and/or (iii) the impact of the upcoming component failure onthe operation of the vehicle 12 (e.g., is the component a major or minorcomponent, can the vehicle 12 continue to operate after the componentfailure, can the component failure cause additional damage to othercomponents, etc.).

In an embodiment, the severity of the upcoming component failure can bedetermined by the threshold that has been crossed using the predictivemodel PM. For example, the severity can be determined based on themagnitude of how far a threshold has been crossed. In another example,the predictive model PM can include multiple thresholds (e.g., a firstthreshold indicating the vehicle can drive 50 miles until the componentfailure, a second threshold indicating that the vehicle can drive 25miles until the component failure, etc.)

In an embodiment, which can utilize a single threshold or multiplethresholds, the severity can be based on at least operatingcharacteristic of the vehicle. For example, by dividing data sets intocharacteristic data sets 60C, one or more threshold for severity can beassociated with different operating characteristics, and the specificoperating characteristics used by the vehicle 12 during collection ofsensor data 30 can be considered when processing the sensor data usingthe predictive model. Thus, in an embodiment, the central controller 14and/or the vehicle controller 22 can determine severity based on theoperating characteristics of the vehicle 12, for example, by dividingthe data sets 60 according to operating characteristics and separatelyprocessing the data sets 60 using one or more predictive model PM.

In an embodiment, basing the severity on an operating characteristic caninclude use of at least one historical operating characteristic of thevehicle 12 stored by the vehicle memory 34 at step 123. The historicaloperating characteristics can depend on past driving habits of one ormore driver of the vehicle 12. That is, the severity of a predictedcomponent failure can be estimated based on how the vehicle 12 istypically driven. For example, faster driving or more breaking duringuse of the vehicle 12 can accelerate certain component failures. Thus,the central controller 14 and/or the vehicle controller 22 can determineseverity based on the historical operating characteristics of thevehicle 12, for example, by considering the historical operatingcharacteristics experienced most frequently by the vehicle 12 duringuse, and by considering how those operating characteristics willaccelerate the component failure in the future if the driver continuesto drive the vehicle according to the same patterns. For example, thecentral controller 14 and/or the vehicle controller 22 can predictidentical anomalies for two vehicles using the predictive model PM, butthen predict one vehicle's component failure will occur sooner due tohow that vehicle's historical operating characteristics.

In an embodiment, the central controller 14 and/or the vehiclecontroller 22 can determine the severity of the upcoming componentfailure by calculating an estimated time or distance remaining until theupcoming component failure. The calculation of the estimated time ordistance can be based on at least one operating characteristic of thevehicle. The operating characteristic can include a real-time or ahistorical operating characteristic. The estimated time or distanceremaining can be calculated, for example, by the predictive model PMknowing how the vehicle 12 is operated in the past and/or by predictingthat the vehicle 12 will operate in the same way in the future. In anembodiment, the estimated time or distance remaining can be calculated,for example, by determining that an anomaly exists using the predictivemodel, and by then considering how quickly the component failure willoccur based on past data, one or more additional physics-based model,and/or future predictions using the operating characteristics. Inanother embodiment, the estimated time or distance remaining can becalculated by dividing the data sets 60 into characteristic data sets60C during step 124, and then processing the characteristic data sets60C using predictive models PM which have been developed for similarcharacteristic data sets 60C based on other vehicles V which experiencedsimilar operating characteristics.

In an embodiment, an output of the predictive model PM can be used withone or more additional physics-based model to determine the severity ofthe upcoming component failure. For example, an additional physics-basedmodel can include a chart which predicts the severity of the upcomingcomponent failure based on the particular anomaly detected by thepredictive model PM, one or more threshold of the predictive model PMbeing crossed for a period of time, and/or the magnitude of how far athreshold has been crossed. The additional physics-based model can bedeveloped, for example, using abnormal data sets 60A for abnormal datafrom similar vehicles V.

At step 126, the central controller 14 and/or the vehicle controller 22generates an alert regarding the upcoming component failure determinedusing the predictive model PM. The alert can include an indication ofthe severity of the upcoming component failure as determined at step125. The indication of the severity can include, for example, one ormore of: (i) the likelihood of the upcoming component failure; (ii) theexpected time or distance remaining until the upcoming componentfailure; and/or (iii) the impact of the upcoming component failure onthe operation of the vehicle 12.

In an embodiment, the central controller 14 and/or the vehiclecontroller 22 can utilize the navigation system 28 and/or GPS device 42to generate the alert. Here, depending on the severity of the componentfailure, the central controller 14 or the vehicle controller 22 candetermine the distance that the vehicle 12 can continue to drive untilthe component failure occurs. Based on the location of the vehicle 12 asdetermined using the GPS device 42, the central controller 14 and/or thevehicle controller 22 can locate repair shops (e.g., car dealerships,third party repair shops, etc.) within that distance and use thenavigation system 28 to route the vehicle 12 to a repair shop that canfix the component failure before it occurs. For example, if a componentfailure will occur in five (5) miles, then the central controller 14and/or the vehicle controller 22 can find a repair shop within five (5)miles and cause the navigation system 28 to route the vehicle 12 to thatlocation. In this way, the systems and methods discussed herein can helpfix a problem before it becomes more expensive.

At step 127, the central controller 14 and/or the vehicle controller 22causes the alert regarding the upcoming component failure to be providedto the driver of the vehicle 12. Here, the vehicle controller 22 cancause the alert to be displayed on the display 38 of the user interface24. The vehicle controller 22 can also cause the alert to be provided tothe driver by other methods, for example, by an audio signal.Additionally or alternatively, at least one of the central controller 14or the vehicle controller 22 can cause the alert to be sent to thepersonal electronic device (e.g, phone, tablet, personal computer, etc.)of the owner of the vehicle 12, thus alerting the owner even if theowner is not in the vehicle at that time.

When the alert is provided to the driver of the vehicle, the alert caninclude a notification of the severity of the upcoming componentfailure. In an embodiment, the alert can indicate the severity byaltering the display 38 based on the severity. The severity can beindicated, for example, by altering a font color or size of the alert onthe display 38 and/or by altering the type of message on the display 38.The severity can also be indicated, for example, by notifying the driverof one or more of: (i) the likelihood of the upcoming component failure;(ii) the time or distance remaining until the upcoming componentfailure; and/or (iii) the impact of the upcoming component failure onthe operation of the vehicle 12 (e.g., whether the vehicle 12 cancontinue to operate after the component failure). The severity can alsobe indicated, for example, by instructing the driver to proceed to arepair shop as discussed above.

FIGS. 3 to 5 illustrate more detailed example embodiments of the method100 of FIG. 2. In each of FIGS. 3 to 5, the reference numerals frommethod 100 have been used to show correspondence with the steps ofmethod 100. It should be understood from this disclosure that method 100can include one or more of the steps of method 200, method 300 and/ormethod 400, alone or in combination, without departing from the spiritand scope of the present disclosure. It should further be understoodfrom this disclosure that method 200, method 300 and method 400 areexample implementations of method 100 and do not limit method 100 to theimplementations described.

FIG. 3 illustrates a first example embodiment of a method 200 forpredicting component failure in a vehicle 12 and alerting a driver. Someor all of the steps of method 200 can be stored as instructions on thevehicle memory 34 and/or the central memory 54 and can be executed bythe vehicle processor 32 and/or the central processor 52 in accordancewith the respective instructions stored on the vehicle memory 34 and/orthe central memory 54. It should be understood from this disclosure thatsome of the steps described herein can be reordered or omitted withoutdeparting from the spirit or scope of method 200.

At step 202, the central controller 14 collects sensor data 30 from aplurality of vehicles V. The vehicles V can be the same or a similarmake and/or model as the vehicle 12 for which the method 200 will beapplied. Alternatively, the vehicle 12 can be among a subset of thevehicles V. For each trip that a vehicle V takes, sensor data 30 can becollected and associated with one or more operating characteristic forthat trip.

At step 204, the central controller 14 can sort the sensor data 30 intoa plurality of data sets 60. If the vehicle 12 is among a subset of thevehicles V for which sensor data 30 was collected, the sensor data 30from vehicles V the same as or similar to the vehicle 12 can first beseparated. Then, in various embodiments, the sensor data 30 from similarvehicles V can be sorted by one or more of: (i) time period ofcollection; (ii) normal or abnormal operation of the vehicle duringcollection; and (iii) one or more operating characteristic sensed duringcollection. These various embodiments are explained in more detail abovewith respect to step 114 of method 100.

At step 206, the central controller 14 can develop covariance data usingthe sensor data 30 generated during normal operation of a vehicle V(e.g., using normal data sets 60N). In developing the covariance data,the central controller 14 can establish whether a correlation existsbetween multiple pairs of sensor data 30 during normal operation of avehicle V. For example, the central controller 14 can establish whethera correlation exists between pairs for sensors 26 for each normal dataset 60N. In an embodiment, the correlation can be a YES or NOcorrelation (e.g., either a correlation exists or does not exist betweena pair of sensors 26 within a data set 60). Whether or not a correlationexists between a pair of sensors 26 can be determined according to asimilar pattern over a time interval and/or according to a predeterminedthreshold for change. In an embodiment, a degree of correlation can alsobe considered.

At step 208, the central server 14 can create a plurality of images 62based on the sensor data 30 from the data sets 60. In an embodiment, theimages 62 can include the covariance data from step 206, such that eachimage 62 represents a plurality of correlations between pairs of sensors26 for a specific time interval and/or for a data set 60. For example, afirst image 62 a can relate to whether a plurality of correlations didor did not occur over a first time period, a second image 62 b canrelate whether a plurality of correlations did or did not occur over asecond time period, etc. FIG. 3 at step 208 illustrates an exampleembodiment of such images 62, wherein a first image 62 a capturescorrelation data for a first time period, and a second image 62 bcaptures correlation data for a second time period. In an embodiment,the first time period can be that of a first data set 60 a, and thesecond time period can be that of a second data set 60 b. The images 62a, 62 b shown in FIG. 3 include covariance plots (here, each imageincludes an 80×80 covariance plot for the example of eighty (80) sensors26 discussed above). Each pixel in the image 62 represents either acorrelation or no correlation between two sensors 26, with a black pixelindicating no correlation (e.g., black=0) and a white pixel indicating acorrelation (e.g., white=1). In an embodiment, the image 62 can be adata matrix of correlations (e.g., a matrix of 0's and l's).

At step 210, the central server 14 can process the images 62 to generateand/or train a predictive model PM. The central server 14 can processthe images 62, for example, by performing an image change detectionalgorithm using a plurality of the images 62. Using the image changedetection algorithm, the central server 14 can determine a normal amountof changes in the images 62 over a period of time (e.g., the number ofpixels which change over a period of time, the location of correlationswhich change over a period of time, etc.). Using this information, thecentral server 14 can determine a threshold for change that the sensors26 experience during periods of normal operation, such that changeswithin the threshold should not raise any alarms or indicate an anomaly.In an embodiment, the central server 14 can separately process images 62for characteristic data sets 60C related to different operatingcharacteristics to determine different thresholds for change dependingon the operating characteristics. In an embodiment, the central server14 can also process the abnormal data sets 60A to determine thresholdsand/or image patterns related to various vehicle component failures, forexample, which occur during various operating conditions.

In an embodiment, the predictive model PM includes a neural networkconfigured to analyze the images 62 for likeness. For example, theneural network can be trained to recognize differences in pixels orgroups of pixels in various images 62. The neural network can further betrained to distinguish normal data from abnormal data based on thedifferences in pixels or groups of pixels between normal data sets 60Nand abnormal data sets 60A. Thus, the neural network can be trained,using similar images 62 that led to specific component failures in avehicle V, to predict a specific upcoming component failure in thevehicle 12 for which the method 200 is applied. The neural network canfurther be trained to use characteristic data sets 60C to predictupcoming vehicle failure depending on the operating characteristicsexperienced by the vehicle 12, for example, due to the driving style ofthe driver.

In an embodiment, the threshold for detection of an anomaly can bedetermined based on clusters of pixels within the images 62. Forexample, clusters can be defined for sets of covariance values. Acluster number can be assigned to each pixel. Then, the change of thecluster number per pixel can be counted to determine the threshold fordetermining an anomaly and/or the severity of an upcoming componentfailure when processing future images 62 using the predictive model PM.It should be understood from this disclosure that the thresholds candiffer for different vehicles 12, different vehicle components within avehicle 12, and/or different operating characteristics for the vehicle12.

At step 212, the predictive model PM is stored. The predictive model PMcan include a neural network that has been trained using the images 62at step 210. The predictive model can be stored, for example, using thevehicle memory 34 or the central memory 54.

At step 214, a vehicle 12 begins to generate sensor data 30. In anembodiment, initial sensor data 30 generated by the sensors 26 of thevehicle 12 can be used to calibrate the predictive model PM as discussedabove with respect to steps 121 and 122 of method 100 (not shown in FIG.3). Alternatively, the predictive model PM can be generic to the vehicle12 and require no further calibration. If the predictive model PM isstored by the central memory 54, then the vehicle controller 14 cantransmit the sensor data 30 to the central controller 14 regularly or atintervals. Alternatively, the predictive model PM can be transferredfrom the central memory 54 to the vehicle memory 34 and stored thereon.

At step 216, either the central controller 14 or the vehicle controller22 processes the sensor data 30 from the vehicle 12 using the predictivemodel PM. The data can be processed in the same way as discussed abovewith respect to steps 204 to 210. That is, the sensor data 30 can beseparated into data sets 60, correlations between pairs of sensors 26 inthe data sets 60 can be determined, and/or the correlations can be usedto create real-time images 62 representative of the real-time sensordata 30 from the vehicle 12. The predictive model PM can then determinehow the real-time images 62 change over time, determine the magnitude ofchanges in the real-time images 62 (e.g., by counting the changes inpixels and/or clusters within the real-time images 62), and/or determinewhether the magnitude of changes crosses the threshold for normalbehavior as determined at step 210.

In one embodiment, either the central controller 14 or the vehiclecontroller 22 can use the predictive model PM to determine whether thecorrelations from the real-time images 62 fall within the thresholds fornormal behavior. For example, the predictive model PM can determinewhether a magnitude of change in the real-time images 62 crosses thethreshold for normal behavior over a period of time as determined atstep 210. If the threshold for normal behavior has been crossed over aperiod of time, then the predictive model PM has detected an anomaly andcan predict that an upcoming vehicle component failure will occur.

In another embodiment, either the central controller 14 or the vehiclecontroller 22 can use the predictive model PM to determine the magnitudeof how far the change in the real-time images 62 crossed the thresholdfor normal behavior over a period of time, or to determine that aplurality of thresholds have been crossed. Depending on the outcome, themagnitude of the difference determined by the predictive model PM can beused, for example, with an additional physics-based model to determinethe severity of the upcoming vehicle component failure.

In another embodiment, either the central controller 14 or the vehiclecontroller 22 can use the predictive model PM to determine which vehiclecomponent will fail based on likeness with abnormal data sets 60A usedin training at step 210. For example, a neural network can be trainedwith images 62 generated from abnormal data sets 60A, and can thereforedetermine whether the real-time images 62 from the vehicle 12 are moresimilar to images 62 related to abnormal data sets 60A than to images 62related to normal data sets 60N. If a real-time image 62 is determinedto be abnormal, then the neural network can determine the specificcomponent failure based on the component failure that occurred when thatabnormal data set 60A was recorded by the previous vehicle V.

At step 218, either the central controller 14 and/or the vehiclecontroller 22 can determine the severity of an upcoming componentfailure. The severity can be determined, for example, as discussed abovewith respect to step 125 of method 100. For example, the severity can bedetermined based on the magnitude of changes in the real-time images 62as determined by the predictive model PM, based on how far the changesin the real-time images 62 cross the threshold for a period of time,based on a plurality of thresholds having been crossed, by usingcharacteristic data sets 60C when processing with the predictive modelPM, and/or by using one or more historical operating characteristic forthe vehicle 12 (e.g., stored by the vehicle memory 34).

At step 220, either the central controller 14 and/or the vehiclecontroller 22 can generate an alert for the driver of the vehicle 12.The alert can be generated, for example, as discussed above with respectto step 126 of method 100.

At step 222, either the central controller 14 and/or the vehiclecontroller 22 can provide the alert to the driver of the vehicle 12. Thealert can be provided, for example, as discussed above with respect tostep 127 of method 100.

FIG. 4 illustrates a second example embodiment of a method 300 forpredicting component failure in a vehicle 12 and alerting a driver. Someor all of the steps of method 300 can be stored as instructions on thevehicle memory 34 and/or the central memory 54 and can be executed bythe vehicle processor 32 and/or the central processor 52 in accordancewith the respective instructions stored on the vehicle memory 34 and/orthe central memory 54. It should be understood from this disclosure thatsome of the steps described herein can be reordered or omitted withoutdeparting from the spirit or scope of method 300.

At step 302, the central controller 14 collects sensor data 30 from aplurality of vehicles V. The vehicles V can be the same or a similarmake and/or model as the vehicle 12 for which the method 200 will beapplied. Alternatively, the vehicle 12 can be among a subset of thevehicles V. For each trip that a vehicle V takes, sensor data 30 can becollected and associated with one or more operating characteristic forthat trip.

At step 304, the central controller 14 can sort the sensor data 30 intoa plurality of data sets 60. If the vehicle 12 is among a subset of thevehicles V for which sensor data was collected, the sensor data 30 fromvehicles V the same as or similar to the vehicle 12 can first beseparated. Then, in various embodiments, the sensor data 30 from similarvehicles V can be sorted by one or more of: (i) time period ofcollection; (ii) normal or abnormal operation of the vehicle duringcollection; and (iii) one or more operating characteristic sensed duringcollection. These various embodiments are explained in more detail abovewith respect to step 114 of method 100.

At step 306, the central controller 14 can develop covariance data usingthe sensor data 30 generated during normal operation of a vehicle V(e.g., using normal data sets 60N). In developing the covariance data,the central controller 14 can establish whether a correlation existsbetween multiple pairs of sensor data 30 during normal operation of avehicle V. For example, the central controller 14 can establish whethera correlation exists between pairs for sensors 26 for each normal dataset 60N. In an embodiment, the correlation can be a YES or NOcorrelation (e.g., either a correlation exists or does not exist betweena pair of sensors 26 within a data set 60). Whether or not a correlationexists between a pair of sensors 26 can be determined according to asimilar pattern over a time interval and/or according to a predeterminedthreshold for change. In an embodiment, a degree of correlation can alsobe considered.

At step 308, the central server 14 can develop a database of graphs 64using the covariance data determined at step 306. Each graph 64 canrepresent, for example, a plurality of correlations between pairs ofsensors 26 for a specific time interval. The graphs 64 can be formedusing a community detection algorithm with data from the data sets 60.More specifically, the graphs 64 can be formed, for example, using acommunity detection algorithm with the covariance data determined fromthe data sets 60. In an embodiment, a graph 64 can be created by formingclusters of nodes 68 with the data points from the data sets 60, and byconnecting correlated data points with a connecting line 66. In anembodiment, the connecting lines 66 can be used to set the thresholdsfor what is normal data.

FIG. 4 at step 308 illustrates an example embodiment of a portion of acommunity detection graph 64. Here, the graph 64 includes three nodes 68which relate to a first sensor 26 a, a second sensor 26 b, and an nthsensor 26 n, respectively. The first sensor 26 a and the second sensor26 b are connected by a connecting line 66, indicating a correlationbetween the first sensor 26 a and the second sensor 26 b. Likewise, thesecond sensor 26 b and the nth sensor 26 n are connected by a connectingline 66, indicating a correlation between the second sensor 26 b and thenth sensor 26 n. On the other hand, the first sensor 26 a and the nthsensor 26 n are not connected by a connecting line 66, indicating nocorrelation between the first sensor 26 a and the nth sensor 26 n. Usinggraphs 64 such as the graph 64 shown in FIG. 4, the number of clustersand the nodes per cluster can be evaluated for different types operatingcharacteristics (e.g., low speed vs. medium speed vs. high speed).

At step 310, the central server 14 can process the graphs 64 to generateand/or train a predictive model PM. The central server 14 can processthe graphs 64, for example, by performing a change detection algorithmusing a plurality of the graphs 64. Using the change detectionalgorithm, the central server 14 can determine a normal amount ofchanges in the graphs 64 over a period of time (e.g., changes in clusternumbers, node numbers, connecting lines 66, etc.). Using thisinformation, the central server 14 can determine a threshold for changethat the sensors 26 experience during periods of normal operation, suchthat changes within the threshold should not raise any alarms orindicate an anomaly. In an embodiment, the central server 14 canseparately process graphs 64 for characteristic data sets 60C related todifferent operating characteristics to determine different thresholdsfor change depending on the operating characteristics. In an embodiment,the central server 14 can also process abnormal data sets 60A todetermine thresholds and/or patterns related to various vehiclecomponent failures, for example, which occur during various operatingconditions.

In an embodiment, the predictive model PM includes a neural networkconfigured to analyze the graphs 64 for likeness. For example, theneural network can be trained to recognize differences in connectinglines 66 in various graphs 64. The neural network can also be trained todistinguish normal data from abnormal data based on the differences inclusters of nodes 88, nodes 88 and/or connecting lines 66. Thus, theneural network can be trained, using similar graphs 64 that led tospecific component failures in a vehicle V, to predict a specificupcoming component failure in the vehicle 12 for which the method 300 isapplied. The neural network can further be trained to use characteristicdata sets 60C to predict upcoming vehicle failure depending on theoperating characteristics experienced by the vehicle 12, for example,due to the driving style of the driver.

At step 312, the predictive model PM is stored. The predictive model PMcan include a neural network that has been trained using the graphs 64at step 310. The predictive model can be stored, for example, using thevehicle memory 34 or the central memory 54.

At step 314, a vehicle 12 begins to generate sensor data 30. In anembodiment, the initial sensor data 30 generated by the sensors 26 ofthe vehicle 12 can be used to calibrate the predictive model PM asdiscussed above with respect to steps 121 and 122 of method 100 (notshown in FIG. 4). Alternatively, the predictive model PM can be genericto the vehicle 12 and require no further calibration. If the predictivemodel PM is stored by the central memory 54, then the vehicle controller14 can transmit the sensor data 30 to the central controller 14regularly or at intervals. Alternatively, the predictive model PM can betransferred from the central memory 54 to the vehicle memory 34 andstored thereon.

At step 316, either the central controller 14 or the vehicle controller22 processes the sensor data 30 using the predictive model PM. The datacan be processed in the same way as discussed above with respect tosteps 304 to 310. That is, the sensor data 30 can be separated into datasets 60, correlations between pairs of sensors 26 in the data sets 60can be determined, and/or the correlations can be used to createreal-time graphs 64 representative of the real-time sensor data 30 fromthe vehicle 12. In an embodiment, clusters of nodes 66 within thereal-time graph 64 can be detected and can be compared to the graphs 64used to train the predictive model PM. In another embodiment, individualnodes within the real-time graph 64 can be detected and can be comparedto the graphs 64 used to train the predictive model PM. In anotherembodiment, connecting lines 66 within the real-time graph 64 can bedetected and can be compared to the graphs 64 used to train thepredictive model PM. In each of these methods, the predictive model PMincludes a baseline for comparison and can determine whether thedeviation from that baseline is within a threshold for normal behavior.

In another embodiment, either the central controller 14 or the vehiclecontroller 22 can use the predictive model PM to determine the magnitudeof the difference between the real-time graphs 64 and the graphs 64 usedto train the predictive model PM. Depending on the outcome, themagnitude of the difference determined by the predictive model PM can beused, for example, with an additional physics-based model to determinethe severity of the upcoming vehicle component failure.

In another embodiment, either the central controller 14 or the vehiclecontroller 22 can use the predictive model PM to determine which vehiclecomponent will fail based on likeness with abnormal data sets 60A usedin training at step 210. For example, a neural network can be trainedwith graphs 64 generated from abnormal data sets 60A, and can thereforedetermine whether the real-time graphs 64 from the vehicle 12 are moresimilar to graphs 64 related to abnormal data sets 60A than to graphs 64related to normal data sets 60N. If a real-time graph 64 is determinedto be abnormal, then the neural network can determine the specificcomponent failure based on the component failure that occurred when thatabnormal data was recorded by the previous vehicle V.

At step 318, either the central controller 14 and/or the vehiclecontroller 22 can determine the severity of an upcoming componentfailure. The severity can be determined, for example, as discussed abovewith respect to step 125 of method 100. For example, the severity can bedetermined based on the magnitude of changes between the real-timegraphs 64 and the previous normal graphs 64 as determined by thepredictive model PM, based on how far the changes in the real-timegraphs 64 cross the threshold over a period of time, based on aplurality of thresholds having been crossed, by using characteristicdata sets 60C when processing with the predictive model PM, and/or byusing one or more historical operating characteristic for the vehicle 12(e.g., stored by the vehicle memory 34).

At step 320, either the central controller 14 and/or the vehiclecontroller 22 can generate an alert for the driver of the vehicle 12.The alert can be generated, for example, as discussed above with respectto step 126 of method 100.

At step 322, either the central controller 14 and/or the vehiclecontroller 22 can provide the alert to the driver of the vehicle 12. Thealert can be provided, for example, as discussed above with respect tostep 127 of method 100.

FIG. 5 illustrates a third example embodiment of a method 400 forpredicting component failure in a vehicle 12 and alerting a driver. Someor all of the steps of method 400 can be stored as instructions on thevehicle memory 34 and/or the central memory 54 and can be executed bythe vehicle processor 32 and/or the central processor 52 in accordancewith the respective instructions stored on the vehicle memory 34 and/orthe central memory 54. It should be understood from this disclosure thatsome of the steps described herein can be reordered or omitted withoutdeparting from the spirit or scope of method 200.

At step 402, the central controller 14 collects sensor data 30 from aplurality of vehicles V. The vehicles V can be the same or a similarmake and/or model as the vehicle 12 for which the method 400 will beapplied. Alternatively, the vehicle 12 can be among a subset of thevehicles V. For each trip that a vehicle V takes, sensor data 30 can becollected and associated with one or more operating characteristic forthat trip.

At step 404, the central controller 14 can sort the sensor data 30 intoa plurality of characteristic data sets 60C. Here, the sensor data 30 isclustered by the characteristic data sets 60C. In FIG. 5, the sensordata 30 is shown clustered by a first characteristic data set 60 a, asecond characteristic data set 60 b, and an nth characteristic data set60 n. Each characteristic data set 60 can include multiple clusters.Each parameter data set 60 can be based on an operating characteristicexperienced by the vehicle V during generation of the sensor data 30.For example, the first characteristic data set 60 a can relate toaverage trip speed and/or acceleration, the second characteristic dataset 60 b can relate to average RPMs, torque or oil temperature, and thenth characteristic data set 60 c can relate to average number of brakingor acceleration events. Here, each characteristic data set 60Crepresents a set of problems (e.g., transmission, fuel system, engine,etc.) Each characteristic data set 60C can include clusters of sensordata points associated with one or more operating characteristic. Thus,each data point within each characteristic data set 60C can include asensor reading and an operating characteristic associated with thesensor reading.

At step 406, the central controller 14 can reduce each cluster withineach characteristic data set 60C to a smaller number of aggregatedsignals. The clusters can be reduced to a smaller number of aggregatedsignals, for example, using a principal component analysis (PCA) (e.g.,a PCA or kernel PCA), a uniform manifold approximation and projection(uMAP), t-distributed scholastic neighbor embedding (t-SNE), and/or thelike. For example, a first characteristic data set 60 a will have aplurality of clusters CA₁, CA₂ . . . CA_(N). Here, each clusters CA₁,CA₂ . . . CA_(N) be reduced to one or more aggregated signals.

At step 408, the central controller 14 can determine the distributionstatistics of each aggregated signal for each cluster in eachcharacteristic data set 60C. The statistics can be determined, forexample, using distribution, mean, variation, correlation, and/orsimilar methods. For example, a first characteristic data set 60 a willhave a plurality of aggregated signals, with each aggregated signalrelated to a cluster. Here, distribution statistics related to eachaggregated signal can be determined such that each aggregated signalwithin each data set has its own distribution statistics.

In a simplified example, sensor data 30 has been divided into a firstcharacteristic data set 60 a (e.g., based on average trip speed and/oracceleration), a second characteristic data set 60 b (e.g., based onaverage temperature, torque or oil temperature), and an nthcharacteristic data set 60 n (e.g., based on an average number ofbraking or acceleration events). Thus, at step 408, the firstcharacteristic data set 60 a can have a plurality of clusters CA₁ . . .CA_(N), with each cluster CA₁ . . . CA_(N) having its own distributionstatistics; the second characteristic data set 60 b can have a pluralityof clusters CB₁ . . . CB_(N), with each cluster CB₁ . . . CB_(N) havingits own distribution statistics; and the nth characteristic data set 60n can have a plurality of clusters CC₁ . . . CC_(N), with each clusterCC₁ . . . CC_(N) having its own distribution statistics.

At step 410, the central controller 14 can process the characteristicdata sets 60C to generate and/or train a predictive model PM. Forexample, the central controller 14 can develop an algorithm using thedistribution statistics for each cluster of each characteristic data set60C. In an embodiment, the predictive model PM can include a neuralnetwork. A neural network approach can be developed, for example, usingautoencoder architecture for the distribution statistics for eachcluster in the parameter data sets 60.

At step 412, the predictive model PM is optimized to duplicate thenormal data, for example, for each cluster within a characteristic dataset 60C and/or for each characteristic data set 60C. In an embodiment,the predictive model PM includes a neural network configured to analyzethe distribution statistics for likeness. The neural network can furtherbe trained to distinguish normal data from abnormal data based on thedistribution statistics. The neural network can further be trained,using distribution statistics that led to specific component failures ina vehicle V, to predict a specific upcoming component failure in thevehicle 12 for which the method 400 is applied. The neural network canfurther be trained to predict upcoming vehicle failure depending on theoperating conditions typically experienced by the vehicle 12, forexample, due to the driving style of the driver.

At step 414, the predictive model PM is stored. The predictive model PMcan include a neural network that has been trained using distributionstatistics at step 410. The predictive model PM can be stored, forexample, using the vehicle memory 34 or the central memory 54.

At step 416, a vehicle 12 begins to generate initial sensor data 30.Step 416 can occur, for example, when the vehicle 12 is new or during afirst predetermined distance driven by the vehicle 12.

At step 418, the initial sensor data 30 generated by the sensors 26 ofthe vehicle 12 can be used to calibrate the predictive model PM storedat step 414. Here, the sensor data 30 can be divided into characteristicdata sets 60C as performed at step 404. Thus, each characteristic dataset 60C can be based on an operating characteristic from the vehicle 12which occurred while the sensor data 30 was generated.

In an embodiment, at step 418, the autoencoder architecture used duringsteps 410 and/or 412 can be applied to the initial sensor data 30 fromthe vehicle 12. For example, the autoencoder architecture can be appliedto distribution statistics for each cluster of each characteristic dataset 60C. In this way, the predictive model PM can be calibrated for thevehicle 12, for example, to account for the setting/sensitivities of thesensors 26. In an embodiment, the calibration procedure can applyweights to inputs in the predictive model PM which are determined fromthe sensor data 30. Alternatively, the calibration procedure can applythe weights to values stored within the predictive model PM so as to fitthe predictive model to the sensors 26 of the vehicle 12. In anotherembodiment, the calibration procedure can apply the weights to thethresholds determined by the predictive model PM and/or in an additionalphysics-based model. By calibrating the predictive model for the vehicle12, the thresholds for anomaly detection can be adjusted to the sensors26 of the vehicle 12.

In an embodiment, the calibration procedure can be used to select a bestmetric for anomaly detection for signals in each cluster of eachcharacteristic data set 60C. For example, the distribution statisticsfor the clusters within the characteristic data sets 60C from theinitial sensor data 30 for the vehicle can be determined usingdistribution, mean, variation, correlation, and/or similar methods.These distribution statistics can be compared to those used to train thepredictive model PM, and the best metric can be used for the calibratedpredictive model PM to establish a threshold for anomaly detection.

At step 420, a vehicle 12 begins normal use and generates sensor data30. The sensor data 30 can be from at least one vehicle sensor 26 of thevehicle 12 over a period of time. For example, the sensor data 30 can begenerated during regular use of the vehicle 12 for day-to-dayactivities. The sensor data 30 can include the same type of sensor data30 used to develop and train the predictive model PM at steps 410 and/or412 and/or the same type of sensor data 30 used to calibrate thepredictive model at step 418.

At step 422, either the central controller 14 or the vehicle controller22 processes the sensor data 30 using the predictive model PM. Thecentral controller 14 or the vehicle controller 22 can process thesensor data 30 to determine that one or more threshold (e.g., determinedat steps 410, 412 and/or 418) has been crossed. In an embodiment, thesensor data 30 can be sorted according to operating characteristics,clustered, and/or used to determine distribution statistics as discussedabove. In an embodiment, the distribution statistics can be processedusing the predictive model PM to determine whether the threshold hasbeen crossed for a period of time. The specific distribution statisticsused for predictive model processing can be chosen, for example, basedon the calibration at step 418.

At step 424, either the central controller 14 and/or the vehiclecontroller can determine the severity of an upcoming component failure.The severity can be determined, for example, as discussed above withrespect to step 125 of method 100. For example, the severity can bedetermined based on the magnitude of the distribution statistics incomparison to the determined threshold for an anomaly, based on how farthe magnitude of the distribution statistics cross the threshold, basedon a plurality of thresholds having been crossed, by using separatethresholds for different characteristic data sets 60C, and/or by usingone or more historical operating characteristic for the vehicle 12(e.g., stored by the vehicle memory 34).

At step 426, either the central controller 14 and/or the vehiclecontroller 22 can generate an alert for the driver of the vehicle 12.The alert can be generated, for example, as discussed above with respectto step 126 of method 100.

At step 428, either the central controller 14 and/or the vehiclecontroller 22 can provide the alert to the driver of the vehicle 12. Thealert can be provided, for example, as discussed above with respect tostep 127 of method 100.

Various steps described above for method 100, and for methods 200, 300and 400 which are example embodiments of method 100, can be performed byeither the central controller 14 and/or the vehicle controller 22without departing from the spirit or scope of the present disclosure.FIGS. 6 to 9 illustrate alternative embodiments of structure that can beused to implement the methods disclosed herein.

FIG. 6 illustrates an example embodiment of a vehicle controller 22Awithin a vehicle 12A in which the central server 14 stores thepredictive model PM and processes the sensor data 30. Here, the centralcontroller 14 is in wireless communication with the vehicle controller22A. In this embodiment, the sensor data 30 includes CAN signals whichare transmitted from the vehicle controller 22A to the centralcontroller 14, so that the central controller 14 can determine when avehicle component failure will occur according to the methods discussedherein. The central controller 14 can then generate the alert for thedriver of the vehicle (e.g., the “Message on Component Failure”) andcause the alert to be displayed on the user interface 24 via wirelesscommunication with the vehicle controller 22A.

FIG. 7 illustrates an example embodiment of a vehicle controller 22Bwithin a vehicle 12B in which the vehicle controller 22B stores thepredictive model PM and processes the sensor data 30. Here, the vehiclecontroller 22B uses a telematics applications processor 70 to store thepredictive model PM. In this embodiment, the sensor data 30 includes CANsignals which are transmitted to the telematics applications processor70, so that the telematics application processor 70 can determine when avehicle component failure will occur according to the methods discussedherein. The telematics application processor 70 can also generate thealert for the driver of the vehicle and cause the alert to be displayedon the user interface 24.

FIG. 8 illustrates another example embodiment of a vehicle controller22C within a vehicle 12C in which the vehicle controller 22C stores thepredictive model PM and processes the sensor data 30. Here, the vehiclecontroller 22 uses a telematics applications processor 70 and a FieldProgrammable Gate Array (“FPGA”) coprocessor 72. The FPGA coprocessor 72can be optimized for neural networks and can store the predictive modelPM including any neural networks associated therewith. In thisembodiment, the sensor data 30 includes CAN signals which aretransmitted to the FPGA coprocessor 72, so that the FPGA coprocessor 72can determine when a vehicle component failure will occur according tothe methods discussed herein. The telematics application processor 70,which is in operative communication with the FPGA coprocessor 72, canthen generate the alert for the driver of the vehicle and cause thealert to be displayed on the user interface 24 based on thedetermination made by the FPGA coprocessor 72.

FIG. 9 illustrates another example embodiment of a vehicle controller22D within a vehicle 12D in which the vehicle controller 22D stores thepredictive model and processes the sensor data 30. Here, the vehiclecontroller 22 includes a telematics application processor 70, a FieldProgrammable Gate Array (“FPGA”) coprocessor 72, and a separate centralprocessing unit (“CPU”) 74. The FPGA coprocessor 72 can be optimized forneural networks and can operate in combination with the CPU 74 to storethe predictive model PM including any neural networks associatedtherewith. In this embodiment, the sensor data 30 includes CAN signalswhich are transmitted to the FPGA coprocessor 72 and/or CPU 74, so thatthe FPGA coprocessor 72 in combination with the CPU 74 can determinewhen a vehicle component failure will occur according to the methodsdiscussed herein. The telematics application processor 70, which is inoperative communication with the FPGA coprocessor 72 and/or CPU 74, canthen generate the alert for the driver of the vehicle and cause thealert to be displayed on the user interface 24 based on thedetermination made by the FPGA coprocessor 70.

FIGS. 6 to 9 further illustrate other components which can be includedwithin or associated with a vehicle controller 22. For example, a datatransmission device 36 can include one or more of a wifi system 80, awireless modem 81 and/or a Bluetooth low energy (“BLE”) system 82, eachof which can be associated with an antenna 83. A data transmissiondevice 36 and/or a GPS device 42 can include a positioning receiver 84and/or a GNSS antenna 85. A vehicle memory 34 can include, for example,one or more of an SDRAM system 86, a flash system 87, and a memorysystem 88. A vehicle processor 32 can include, for example, a voltageregulator 89 in communication with a positive battery and/or a bustransceiver 90 in communication with a data switch 91. The vehicleprocessor 32 can further be placed in communication with motion sensors92 which can be included within or be separate from the sensors 26discussed herein. Those of ordinary skill in the art should recognizefrom this disclosure that these are examples only, which are illustratedfor the purpose of explaining that there are multiple configurationswhich can be used to implement the present disclosure.

As illustrated, FIG. 6 illustrates an example embodiment in which thecentral server 14 stores the predictive model PM and processes thesensor data 30, whereas FIGS. 7 to 9 illustrate example embodiments inwhich the vehicle controller 22 stores the predictive model PM andprocesses the sensor data 30. By locating the predictive model PMlocally on the vehicle 12, as shown in FIGS. 7 to 9, wirelesstransmission costs from the vehicle 12 can be advantageously reduced.Alternatively, by transmitting sensor data 30 to the central processor14, the generic predictive model PM stored by the central controller 14can be constantly updated and improved.

GENERAL INTERPRETATION OF TERMS

In understanding the scope of the present invention, the term“comprising” and its derivatives, as used herein, are intended to beopen ended terms that specify the presence of the stated features,elements, components, groups, integers, and/or steps, but do not excludethe presence of other unstated features, elements, components, groups,integers and/or steps. The foregoing also applies to words havingsimilar meanings such as the terms, “including”, “having” and theirderivatives. Also, the terms “part,” “section,” “portion,” “member” or“element” when used in the singular can have the dual meaning of asingle part or a plurality of parts.

The term “configured” as used herein to describe a component, section orpart of a device includes hardware and/or software that is constructedand/or programmed to carry out the desired function.

The term “processor” as used herein can refer to one or more processors,such as one or more special purpose processors, one or more digitalsignal processors, one or more microprocessors, and/or one or more otherprocessors as known in the art.

The term “memory” as used herein can refer to any computer useable orcomputer readable medium or device that can contain, store, communicate,or transport any signal or information that can be used with anyprocessor. For example, a memory can include one or more read onlymemory (ROM), random access memory (RAM), one or more other memory,and/or combinations thereof.

While only selected embodiments have been chosen to illustrate thepresent invention, it will be apparent to those skilled in the art fromthis disclosure that various changes and modifications can be madeherein without departing from the scope of the invention as defined inthe appended claims. For example, the size, shape, location ororientation of the various components can be changed as needed and/ordesired. Components that are shown directly connected or contacting eachother can have intermediate structures disposed between them. Thefunctions of one element can be performed by two, and vice versa. Thestructures and functions of one embodiment can be adopted in anotherembodiment. It is not necessary for all advantages to be present in aparticular embodiment at the same time. Every feature which is uniquefrom the prior art, alone or in combination with other features, alsoshould be considered a separate description of further inventions by theapplicant, including the structural and/or functional concepts embodiedby such feature(s). Thus, the foregoing descriptions of the embodimentsaccording to the present invention are provided for illustration only,and not for the purpose of limiting the invention as defined by theappended claims and their equivalents.

What is claimed is:
 1. A method for predicting component failure in avehicle and alerting a driver based thereon, the method comprising:receiving sensor data from at least one vehicle sensor over a period oftime; processing the sensor data using a predictive model to detect ananomaly indicative of an upcoming component failure; determining aseverity of the upcoming component failure based on at least oneoperating characteristic of the vehicle; and providing an alert to thedriver of the vehicle regarding the severity of the upcoming componentfailure.
 2. The method of claim 1, further comprising training thepredictive model to detect the anomaly using time-series data from aplurality of similar vehicles.
 3. The method of claim 1, furthercomprising storing the predictive model on a memory of the vehicle. 4.The method of claim 1, wherein determining the severity of the upcomingcomponent failure includes calculating an estimated time or distanceremaining until the upcoming component failure based on the at least oneoperating characteristic of the vehicle.
 5. The method of claim 4,wherein providing the alert to the driver includes notifying the driverof the estimated time or distance remaining until the upcoming componentfailure.
 6. The method of claim 1, wherein the at least one operatingcharacteristic includes at least one of: (i) average trip speed of thevehicle; (ii) average acceleration of the vehicle; (iii) averagerevolutions per minute experienced by the vehicle; (iv) average torqueexperienced by the vehicle; (v) average oil temperature experienced bythe vehicle; and (vi) average number of braking or acceleration eventsexperienced by the vehicle.
 7. The method of claim 1, further comprisingseparating the sensor data into a plurality of characteristic data sets,and determining covariance data based on multiple of the plurality ofcharacteristic data sets, wherein processing the sensor data using thepredictive model includes processing the covariance data using thepredictive model.
 8. The method of claim 1, further comprising creatingat least one image using the sensor data, wherein processing the sensordata using the predictive model includes processing the at least oneimage using the predictive model.
 9. A method for predicting componentfailure in a vehicle and alerting a driver based thereon, the methodcomprising: receiving sensor data from at least one vehicle sensor overa period of time; separating the sensor data into a plurality ofcharacteristic data sets corresponding to different operatingcharacteristics of the vehicle; processing at least one of thecharacteristic data sets using a predictive model to detect an anomalyindicative of an upcoming component failure; and providing an alert tothe driver of the vehicle regarding the upcoming component failure. 10.The method of claim 9, further comprising creating at least one imageusing at least one of the characteristic data sets, wherein processingat least one of the characteristic data sets using the predictive modelincludes processing the at least one image using the predictive model.11. The method of claim 10, wherein the at least one image is createdusing multiple of the plurality of characteristic data sets.
 12. Themethod of claim 10, wherein the at least one image is created usingcovariance data based on multiple of the plurality of characteristicdata sets.
 13. The method of claim 9, wherein separating the sensor datainto the plurality of characteristic data sets includes separating thesensor data into the plurality of characteristic data sets based oncorrespondence with at least one of: (i) trip speed; (ii) acceleration;(iii) revolutions per minute; (iv) torque; (v) oil temperature; and (vi)number of braking or acceleration events.
 14. The method of claim 9,wherein processing at least one of the characteristic data sets usingthe predictive model includes processing covariance data based onmultiple of the characteristic data sets using the predictive model. 15.The method of claim 9, further comprising training the predictive modelto detect the anomaly using time-series data from a plurality of similarvehicles.
 16. The method of claim 9, further comprising calculating anestimated time or distance remaining until the upcoming componentfailure, wherein providing the alert to the driver includes notifyingthe driver of the estimated time or distance remaining until theupcoming component failure.
 17. A system for predicting componentfailure in a vehicle and alerting a driver based thereon, the systemcomprising: at least one vehicle sensor configured to generate sensordata relating to at least one vehicle component over a period of time; amemory storing at least one operating characteristic of the vehicle andat least one predictive model that has been trained using time-seriesdata from a plurality of other vehicles; and a processor configured toexecute instructions stored on the memory to: (i) process the sensordata using the predictive model to detect an anomaly indicative of anupcoming component failure; (ii) determine a severity of the upcomingcomponent failure based on the at least one operating characteristic ofthe vehicle; and (iii) provide an alert to the driver of the vehicleregarding the severity of the upcoming component failure.
 18. The systemof claim 17, wherein at least one of the processor and the memory islocated within the vehicle.
 19. The system of claim 17, wherein theprocessor is configured to cluster the sensor data in a plurality ofcharacteristic data sets corresponding to different operatingcharacteristics of the vehicle, and to process at least one of thecharacteristic data sets using the predictive model to detect theanomaly indicative of the upcoming component failure.
 20. The system ofclaim 17, wherein the vehicle includes a display, and the processorprovides the alert to the driver by altering the display based on theseverity of the upcoming component failure.